{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:HQL3VHFUEVKNGQL6QCYUA3GZG6","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"88376b7627f5357e21187dde1794528d7ec4891c45f31bfe8e3347cf4a462bd2","cross_cats_sorted":["cs.AI","cs.CL"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-03-07T03:38:44Z","title_canon_sha256":"1daccd95b758f4d1904da38054064c024fac9c9d86ef4a3bc76f320024b6c8a8"},"schema_version":"1.0","source":{"id":"2403.04190","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2403.04190","created_at":"2026-07-05T07:53:19Z"},{"alias_kind":"arxiv_version","alias_value":"2403.04190v1","created_at":"2026-07-05T07:53:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.04190","created_at":"2026-07-05T07:53:19Z"},{"alias_kind":"pith_short_12","alias_value":"HQL3VHFUEVKN","created_at":"2026-07-05T07:53:19Z"},{"alias_kind":"pith_short_16","alias_value":"HQL3VHFUEVKNGQL6","created_at":"2026-07-05T07:53:19Z"},{"alias_kind":"pith_short_8","alias_value":"HQL3VHFU","created_at":"2026-07-05T07:53:19Z"}],"graph_snapshots":[{"event_id":"sha256:0cfdc58fcbc0b918adec66965d8e5c9c63e00de2c6ce8304d11348ee3f725cf5","target":"graph","created_at":"2026-07-05T07:53:19Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2403.04190/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The recent surge in research focused on generating synthetic data from large language models (LLMs), especially for scenarios with limited data availability, marks a notable shift in Generative Artificial Intelligence (AI). Their ability to perform comparably to real-world data positions this approach as a compelling solution to low-resource challenges. This paper delves into advanced technologies that leverage these gigantic LLMs for the generation of task-specific training data. We outline methodologies, evaluation techniques, and practical applications, discuss the current limitations, and ","authors_text":"Xu Guo, Yiqiang Chen","cross_cats":["cs.AI","cs.CL"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-03-07T03:38:44Z","title":"Generative AI for Synthetic Data Generation: Methods, Challenges and the Future"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.04190","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:f70712c6f6d6488255baaf568a590fac94d65f1d44bf6a20e87a3e742f37d8c6","target":"record","created_at":"2026-07-05T07:53:19Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"88376b7627f5357e21187dde1794528d7ec4891c45f31bfe8e3347cf4a462bd2","cross_cats_sorted":["cs.AI","cs.CL"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-03-07T03:38:44Z","title_canon_sha256":"1daccd95b758f4d1904da38054064c024fac9c9d86ef4a3bc76f320024b6c8a8"},"schema_version":"1.0","source":{"id":"2403.04190","kind":"arxiv","version":1}},"canonical_sha256":"3c17ba9cb42554d3417e80b1406cd9379e1ee8a7b65c5f2db2a6c09573f13252","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3c17ba9cb42554d3417e80b1406cd9379e1ee8a7b65c5f2db2a6c09573f13252","first_computed_at":"2026-07-05T07:53:19.757485Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:53:19.757485Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"s8D1R3PreF1jU1qIuawzkp2bkZPYdJ7mECs0rkApi9S8HHmA9q7zWanWdxKmKRqLb5pCoIJsuJplYVMkY12hBg==","signature_status":"signed_v1","signed_at":"2026-07-05T07:53:19.757914Z","signed_message":"canonical_sha256_bytes"},"source_id":"2403.04190","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f70712c6f6d6488255baaf568a590fac94d65f1d44bf6a20e87a3e742f37d8c6","sha256:0cfdc58fcbc0b918adec66965d8e5c9c63e00de2c6ce8304d11348ee3f725cf5"],"state_sha256":"68c101b635c08fbc97d646aa575493098b49c58c9a6f43094047858635762500"}